Physicochemical properties of bacterial cellulose obtained from different Kombucha fermentation conditions

The production of bacterial cellulose has been limited due to its high cost and low productivity. Alternative low‐cost sources of this biopolymer of high purity and biocompatibility are needed in order to benefit from its enormous potential. Kombucha tea is a trend functional beverage whose production is growing exponentially worldwide, and the bacteria present in this fermented beverage belonging to the genus Komagataeibacter are capable of producing a crystalline biofilm with interesting properties. Obtaining bacterial cellulose from Kombucha tea has already been studied, however several fermentation conditions are being optimized in order to scale‐up its production. In this study, we characterized the bacterial cellulose produced from three different Kombucha fermentation conditions. The scanning electron microscopy images revealed the crystalline structure of the biofilms. The energy‐dispersive x‐ray analysis exhibited the chemical composition of the crystals. The thermogravimetric analysis showed a rate of degradation between 490 and 560°C and the differential scanning calorimetry confirmed the presence of crystalline and amorphous regions in the bacterial cellulose samples. The results suggested that crystalline cellulose could be obtained by varying the fermentation conditions of Kombucha tea

Design, upgrade and characterization of the silicon photomultiplier front-end for the AMIGA detector at the Pierre Auger Observatory

AMIGA (Auger Muons and Infill for the Ground Array) is an upgrade of the Pierre Auger Observatory to complement the study of ultra-high-energy cosmic rays (UHECR) by measuring the muon content of extensive air showers (EAS). It consists of an array of 61 water Cherenkov detectors on a denser spacing in combination with underground scintillation detectors used for muon density measurement. Each detector is composed of three scintillation modules, with 10 m$^2$ detection area per module, buried at 2.3 m depth, resulting in a total detection area of 30 m$^2$. Silicon photomultiplier sensors (SiPM) measure the amount of scintillation light generated by charged particles traversing the modules. In this paper, the design of the front-end electronics to process the signals of those SiPMs and test results from the laboratory and from the Pierre Auger Observatory are described. Compared to our previous prototype, the new electronics shows a higher performance, higher efficiency and lower power consumption, and it has a new acquisition system with increased dynamic range that allows measurements closer to the shower core. The new acquisition system is based on the measurement of the total charge signal that the muonic component of the cosmic ray shower generates in the detector.Comment: 40 pages, 33 figure

Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks

The Pierre Auger Observatory, at present the largest cosmic-ray observatory ever built, is instrumented with a ground array of 1600 water-Cherenkov detectors, known as the Surface Detector (SD). The SD samples the secondary particle content (mostly photons, electrons, positrons and muons) of extensive air showers initiated by cosmic rays with energies ranging from $10^{17}~$eV up to more than $10^{20}~$eV. Measuring the independent contribution of the muon component to the total registered signal is crucial to enhance the capability of the Observatory to estimate the mass of the cosmic rays on an event-by-event basis. However, with the current design of the SD, it is difficult to straightforwardly separate the contributions of muons to the SD time traces from those of photons, electrons and positrons. In this paper, we present a method aimed at extracting the muon component of the time traces registered with each individual detector of the SD using Recurrent Neural Networks. We derive the performances of the method by training the neural network on simulations, in which the muon and the electromagnetic components of the traces are known. We conclude this work showing the performance of this method on experimental data of the Pierre Auger Observatory. We find that our predictions agree with the parameterizations obtained by the AGASA collaboration to describe the lateral distributions of the electromagnetic and muonic components of extensive air showers.Comment: 23 pages, 15 figures. Version accepted for publication in JINS

Design and implementation of the AMIGA embedded system for data acquisition

The Auger Muon Infill Ground Array (AMIGA) is part of the AugerPrime upgrade of the Pierre Auger Observatory. It consists of particle counters buried 2.3 m underground next to the water-Cherenkov stations that form the 23.5 km$^2$ large infilled array. The reduced distance between detectors in this denser area allows the lowering of the energy threshold for primary cosmic ray reconstruction down to about 10$^{17}$ eV. At the depth of 2.3 m the electromagnetic component of cosmic ray showers is almost entirely absorbed so that the buried scintillators provide an independent and direct measurement of the air showers muon content. This work describes the design and implementation of the AMIGA embedded system, which provides centralized control, data acquisition and environment monitoring to its detectors. The presented system was firstly tested in the engineering array phase ended in 2017, and lately selected as the final design to be installed in all new detectors of the production phase. The system was proven to be robust and reliable and has worked in a stable manner since its first deployment.Comment: Accepted for publication at JINST. Published version, 34 pages, 15 figures, 4 table

Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory

The atmospheric depth of the air shower maximum Xmax is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of Xmax are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of Xmax from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of Xmax. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed Xmax using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than 25 g/cm2 at energies above 2×1019 eV

Calibration of the underground muon detector of the Pierre Auger Observatory

To obtain direct measurements of the muon content of extensive air showers with energy above 10$^{16.5}$ eV, the Pierre Auger Observatory is currently being equipped with an underground muon detector (UMD), consisting of 219 10 m$^{2}$-modules, each segmented into 64 scintillators coupled to silicon photomultipliers (SiPMs). Direct access to the shower muon content allows for the study of both of the composition of primary cosmic rays and of high-energy hadronic interactions in the forward direction. As the muon density can vary between tens of muons per m2 close to the intersection of the shower axis with the ground to much less than one per m2 when far away, the necessary broad dynamic range is achieved by the simultaneous implementation of two acquisition modes in the read-out electronics: the binary mode, tuned to count single muons, and the ADC mode, suited to measure a high number of them. In this work, we present the end-to-end calibration of the muon detector modules: first, the SiPMs are calibrated by means of the binary channel, and then, the ADC channel is calibrated using atmospheric muons, detected in parallel to the shower data acquisition. The laboratory and field measurements performed to develop the implementation of the full calibration chain of both binary and ADC channels are presented and discussed. The calibration procedure is reliable to work with the high amount of channels in the UMD, which will be operated continuously, in changing environmental conditions, for several years

Deep-Learning based Reconstruction of the Shower Maximum XmaxX_{\mathrm{max}} using the Water-Cherenkov Detectors of the Pierre Auger Observatory

The atmospheric depth of the air shower maximum $X_{\mathrm{max}}$ is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of $X_{\mathrm{max}}$ are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of $X_{\mathrm{max}}$ from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of $X_{\mathrm{max}}$. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed $X_{\mathrm{max}}$ using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than $25~\mathrm{g/cm^{2}}$ at energies above $2\times 10^{19}~\mathrm{eV}$.Comment: Published version, 29 pages, 12 figure

Status and performance of the underground muon detector of the Pierre Auger Observatory

The Auger Muons and Infill for the Ground Array (AMIGA) is an enhancement of the Pierre Auger Observatory, whose purpose is to lower the energy threshold of the observatory down to 1016.5 eV, and to measure the muonic content of air showers directly. These measurements will significantly contribute to the determination of primary particle masses in the range between the second knee and the ankle, to the study of hadronic interaction models with air showers, and, in turn, to the understanding of the muon puzzle. The underground muon detector of AMIGA is concomitant to two triangular grids of water-Cherenkov stations with spacings of 433 and 750 m; each grid position is equipped with a 30 m2 plastic scintillator buried at 2.3 m depth. After the engineering array completion in early 2018 and general improvements to the design, the production phase commenced. In this work, we report on the status of the underground muon detector, the progress of its deployment, and the performance achieved after two years of operation. The detector construction is foreseen to finish by mid-2022

Direct measurement of the muonic content of extensive air showers between 2×1017\mathbf { 2\times 10^{17}} and 2×1018 \mathbf {2\times 10^{18}}~eV at the Pierre Auger Observatory

The hybrid design of the Pierre Auger Observatory allows for the measurement of the properties of extensive air showers initiated by ultra-high energy cosmic rays with unprecedented precision. By using an array of prototype underground muon detectors, we have performed the first direct measurement, by the Auger Collaboration, of the muon content of air showers between 2×10$^{17}$ and 2×10$^{18}$ eV. We have studied the energy evolution of the attenuation-corrected muon density, and compared it to predictions from air shower simulations. The observed densities are found to be larger than those predicted by models. We quantify this discrepancy by combining the measurements from the muon detector with those from the Auger fluorescence detector at 10$^{17.5}$eV and 10$^{18}$eV. We find that, for the models to explain the data, an increase in the muon density of 38% ±4%(12%) ± (21%)¦(18%) for EPOS-LHC, and of 50%(53%) ±4%(13%) ± (23%)¦(20%) for QGSJetII-04, is respectively needed